mvpa2.kernels.npΒΆ
Kernels for Gaussian Process Regression and Classification.
Functions
squared_euclidean_distance(data1[, data2, ...]) |
Compute weighted euclidean distance matrix between two datasets. |
Classes
ConditionalAttribute([enabled]) |
Simple container intended to conditionally store the value |
ConstantKernel(\*args, \*\*kwargs) |
The constant kernel class. |
EnsureFloat() |
Ensure that an input (or several inputs) are of a data type ‘float’. |
EnsureListOf(dtype) |
Ensure that an input is a list of a particular data type |
ExponentialKernel(\*args, \*\*kwargs) |
The Exponential kernel class. |
GeneralizedLinearKernel(\*args, \*\*kwargs) |
The linear kernel class. |
LinearKernel(\*args, \*\*kwargs) |
Simple linear kernel: K(a,b) = a*b.T |
Matern_3_2Kernel([length_scale, sigma_f, ...]) |
The Matern kernel class for the case ni=3/2 or ni=5/2. |
Matern_5_2Kernel(\*\*kwargs) |
The Matern kernel class for the case ni=5/2. |
NumpyKernel(\*args, \*\*kwargs) |
A Kernel object with internal representation as a 2d numpy array |
Parameter(default[, constraints, ro, index, ...]) |
This class shall serve as a representation of a parameter. |
PolyKernel(\*args, \*\*kwargs) |
Polynomial kernel: K(a,b) = (gamma*a*b.T+coef0)**degree |
RationalQuadraticKernel([length_scale, ...]) |
The Rational Quadratic (RQ) kernel class. |
RbfKernel(\*args, \*\*kwargs) |
Radial basis function (aka Gausian, aka ) kernel |
SquaredExponentialKernel([length_scale, sigma_f]) |
The Squared Exponential kernel class. |
Exceptions
ConditionalAttribute([enabled]) |
Simple container intended to conditionally store the value |
ConstantKernel(\*args, \*\*kwargs) |
The constant kernel class. |
EnsureFloat() |
Ensure that an input (or several inputs) are of a data type ‘float’. |
EnsureListOf(dtype) |
Ensure that an input is a list of a particular data type |
ExponentialKernel(\*args, \*\*kwargs) |
The Exponential kernel class. |
GeneralizedLinearKernel(\*args, \*\*kwargs) |
The linear kernel class. |
LinearKernel(\*args, \*\*kwargs) |
Simple linear kernel: K(a,b) = a*b.T |
Matern_3_2Kernel([length_scale, sigma_f, ...]) |
The Matern kernel class for the case ni=3/2 or ni=5/2. |
Matern_5_2Kernel(\*\*kwargs) |
The Matern kernel class for the case ni=5/2. |
NumpyKernel(\*args, \*\*kwargs) |
A Kernel object with internal representation as a 2d numpy array |
Parameter(default[, constraints, ro, index, ...]) |
This class shall serve as a representation of a parameter. |
PolyKernel(\*args, \*\*kwargs) |
Polynomial kernel: K(a,b) = (gamma*a*b.T+coef0)**degree |
RationalQuadraticKernel([length_scale, ...]) |
The Rational Quadratic (RQ) kernel class. |
RbfKernel(\*args, \*\*kwargs) |
Radial basis function (aka Gausian, aka ) kernel |
SquaredExponentialKernel([length_scale, sigma_f]) |
The Squared Exponential kernel class. |
